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This research investigates the relationship between quantitative lung fibrosis metrics from pretreatment CT scans and the risk of immune checkpoint inhibitor-related pneumonitis in NSCLC patients.

May 30, 2026

Quantitative CT lung fibrosis and immune checkpoint inhibitor–related pneumonitis in NSCLC.

Key Points

This research investigates the relationship between quantitative lung fibrosis metrics from pretreatment CT scans and the risk of immune checkpoint inhibitor-related pneumonitis in NSCLC patients.
Retrospective analysis of 241 metastatic NSCLC patients treated with immune checkpoint inhibitors.
Quantitative analysis performed using VIDA software on CT scans to extract metrics for various fibrosis types.
Univariate logistic regression assessed associations between quantitative fibrosis metrics and pneumonitis risk.
Lower-lobe mass-based metrics for ground-glass opacity (OR 1.005, p=0.030), quantitative ILD (OR 1.004, p=0.031), and quantitative ILA (OR 1.004, p=0.024) were associated with increased pneumonitis risk.
Peripheral (peel) consolidation volume significantly associated with ICI-P (OR 1.102, p=0.023).
Upper-lobe metrics showed no significant associations, indicating regional differences in risk.

Abstract

e20531 Background: Interstitial lung abnormalities (ILAs) are established risk factors for immune checkpoint inhibitor–related pneumonitis (ICI-P), but qualitative ILA assessment is subjective. We evaluated whether quantitative lung fibrosis (QLF) metrics from pretreatment computed tomography (CT) are associated with ICI-P in patients with non–small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). Methods: We retrospectively analyzed 241 patients with metastatic NSCLC treated with ICIs. Pretreatment CT scans underwent quantitative analysis using commercial software (VIDA) to extract QLFs representing consolidation, ground-glass opacity (GGO), emphysema, reticulation, and honeycombing. QLFs were quantified by percentage, volume, and estimated mass at total lung, lobar (upper vs lower), and subregional (core central vs peel peripheral) levels. Associations between QLFs and ICI-P were evaluated using univariate logistic regression. Results: Regional QLFs demonstrated differential associations with ICI-P. Upper-lobe QLFs were not associated with pneumonitis (all p≥0.13). In contrast, lower-lobe mass-based fibrosis metrics were associated with increased ICI-P risk, including GGO mass (OR 1.005, 95% CI 1.001–1.010; p=0.030), quantitative ILD (QILD) mass (OR 1.004, 95% CI 1.000–1.008; p=0.031), and quantitative ILA (QILA) mass (OR 1.004, 95% CI 1.001–1.007; p=0.024). Total-lung QILA mass showed a trend toward association (OR 1.002, 95% CI 1.000–1.004; p=0.086). Subregional analysis demonstrated stronger associations in peripheral (peel) regions; peel consolidation volume was associated with ICI-P (OR 1.102, 95% CI 1.013–1.198; p=0.023), whereas no core metric reached significance. Conclusions: Quantitative fibrosis metrics from pretreatment CT, particularly lower-lobe and peripheral mass-based QLFs, are associated with immune checkpoint inhibitor–related pneumonitis in NSCLC. Incorporation of regional quantitative fibrosis measures into baseline imaging assessment may help identify patients at elevated pneumonitis risk and inform surveillance strategies. These findings support further multivariable validation of quantitative CT fibrosis metrics as imaging biomarkers for immunotherapy-related pneumonitis. Associations between quantitative lung fibrosis metrics and immune checkpoint inhibitor–related pneumonitis in NSCLC. Region Metric (Mass-based QLF) (gm) OR 95% CI p-value Lower lobe GGO Mass 1.005 1.001–1.010 0.030 Lower lobe QILD Mass 1.004 1.000–1.008 0.031 Lower lobe QILA Mass 1.004 1.001–1.007 0.024 Upper lobe No significant predictors — — ≥0.13 Total lung QILA Mass 1.002 1.000–1.004 0.086

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